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English(EN) Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000

神经网络提升 JPEG 2000 压缩效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的方法，通过集成神经网络辅助提升步骤来增强 JPEG 2000 标准内的有损图像压缩。这些附加步骤旨在减少残余冗余并在较低分辨率下提高图像质量。该方法使用紧凑型神经网络，其一组训练参数可应用于所有分解级别和比特率，已证明在保持 JPEG 2000 可伸缩特性的同时，平均比特率节省高达 17.4%。 AI

影响 这项研究可能带来更高效的图像压缩技术，从而影响视觉数据的存储和传输。

排序理由 详细介绍图像压缩新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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神经网络提升 JPEG 2000 压缩效率

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详细介绍图像压缩新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xinyue Li, Aous Naman, David Taubman ·

    用于改进 JPEG 2000 全可伸缩有损图像压缩的神经网络辅助提升步骤

    arXiv:2403.01647v2 Announce Type: replace Abstract: This work proposes to augment the lifting steps of the conventional wavelet transform with additional neural network assisted lifting steps. These additional steps reduce residual redundancy (notably aliasing information) amongs…

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